Agreed. This is pure architectural thinking: you hold the ground truth, enforce the strict IAM boundaries, and outsource the mud-playing to the LLM. Mindless 'vibe coding' without this structural discipline is just tittytainment. The job is contract validation now, not typing.
Guilty as charged. As a non-native speaker, I used an LLM to compile my original thoughts into fluent English.
But notice the irony: I used AI exactly as I advocate. It handled the horizontal spread (syntax), while I rigorously enforced the vertical depth (the architectural logic). The 'taste' is entirely mine.
Thank you to Arainach and cableshaft for engaging with the actual substance. Dismissing a core argument because you pattern-matched an 'em-dash' is exactly the shallow thinking this post warns about.
This is a phenomenal example of exactly what I am advocating.
Notice you didn't ask the AI to 'just design a stereo pedal for me.' You interrogated it, reasoned about netlists, and forced the concepts into your brain through intense friction. That is pure deep work.
Throughout the reverse engineering process, the LLM and I both were expecting each op-amp stage to use the next ladder value capacitor. We'd talked ourselves into how and why that would make sense.
At the end I was curious enough that I desoldered those five caps and realized that they were all 2.2nF except for the last stage which was 1nF.
I brought that news back to the LLM and we realigned our understanding of how the effect was achieved, ultimately coming to realize that our approach would have created notches at different frequencies instead of just shifting the phase by about 900 degrees.
It was an incredible learning experience. I try hard not to personify LLMs but this really did feel like working side by side with a friend on a problem until it was solved.
IRL, I suspect that most people who would be able to tackle that challenge with me lack both the time and patience to actually do it.
Ironically, what you described is exactly using AI to help with deep work. You do the heavy lifting (reading), and use AI strictly for stateless verification and testing your mental model. That is the ideal synergy.
Spot on. The ultimate bottleneck is no longer generation; it's verification.
If you don't intrinsically know what 'right' looks like, AI simply helps you build the wrong thing faster. This internal compass is exactly what I meant by 'taste' in the original post.
Pure 'vibe coding' is essentially technical 'tittytainment'. Using AI for the horizontal spread while you enforce vertical architectural depth is true deep work.
We actually don't disagree at all—you are perfectly illustrating my point.
Applying strict epistemic discipline (Popper, Russell) to resolve ambiguity and accelerate actual practice is the very definition of deep work. You aren't using AI as a shortcut to skip thinking; you're using it as a Socratic sparring partner to deepen it. This is exactly the paradigm shift I'm advocating for.
I’m strongly reminded of early google every time I use AI for research. I used to be able to know little about a topic, try to search on it and get shit results. But, google would give me pages of results. So I could skim a lot and eventually on page 10, I stumble across some term of art, and that term would greatly improve my search. Rinse and repeat, and I’d have a good sense about the topic I was interested in.
You can’t really do that with google anymore, and I can’t remember the last time I bothered to actually learn something that wasn’t trivial from google. ChatGPT, however, has been a game changer. I can ask a really dumb question and get some basic info about the thing I’m asking about, and while it’s often not quite what I’m looking for, it gives me clues to follow, and I can quickly zero in on what I’m looking for, often in new contexts.
As an autodidact who’s main motivation to go to college was to get access to the stacks and direct internet access, I can’t even begin to tell you how game changing LLMs seem to be for learning.
To your point though, my concern is we don’t know how to teach how to learn, and LLMs will likely seduce many into bad behavior and poor research hygiene. I treat my research the same way I attack the stacks, but take someone who’s never been to a research library and ask them to create a report on some topic, and just why? That is the basic resistance, why?, why do what an LLM is almost literally built to do. Yet that is also highly related to individual learning, to take a bunch of disperate sources and synthesize output related to the input.
I suspect we’ll learn how to use LLMs in the same way we learned how to use calculators. But I have no doubt that on average (or maybe median or mode?) calculators have made us less capable to do basic arithmetic, and I suspect LLMs will also cause a great percentage of the population to be worse at sythesizing information. I’d hope that it’s only the same people who would have otherwise only gotten their information from TV, but I do have a slight fear it will creep past that subsection of the population.
I burn through $5,000 a month in API tokens. I am the last person to romanticize manual toil.
The issue is the difference between using AI for shallow outsourcing ('summarize this') and deep cognitive work ('stress-test this architecture'). AI should be a cognitive amplifier for much harder problems, not a shortcut to bypass critical thinking entirely.
Cloudflare’s dual 2025 outages weren't just about a missed Lua nil-check or a sloppy Rust unwrap(). They were the inevitable cost of "hotwiring" production to bypass ancient, broken test harnesses during a security panic.
This article argues that Cloudflare's "Homogeneous Edge" architecture—where every node runs every service for extreme efficiency—has created an infinite failure domain. When commercial constraints demand tight coupling and zero physical isolation, a single "Killswitch" logic error doesn't just fail safe; it becomes a global detonator. A deep dive into how technical debt and architectural gambling eventually cash their checks.
